Accession Number:

ADA509091

Title:

Electromagnetic Models and Inversion Techniques for Multiple UXO Discrimination

Descriptive Note:

Final rept. 1 Jan 2005-31 Dec 2007

Corporate Author:

MASSACHUSETTS INST OF TECH CAMBRIDGE RESEARCH LAB OF ELECTRONICS

Report Date:

2007-12-31

Pagination or Media Count:

23.0

Abstract:

Recovery of buried unexploded ordnance UXO is very slow and expensive due to the high false alarm rate created by clutter. Electromagnetic induction EMI has been shown to be a promising technique for UXO detection and discrimination. We use the EMI response of buried targets to identify or classify them. Given that now a more complete model of the measurable response of a buried UXO is implemented, this study proceeds to demonstrate that EMI responses from UXO and clutter objects can be used to identify the objects through the application of Differential Evolution DE, a type of Genetic Algorithm. DE is used to optimize the parameters of the UXO fundamental mode model to produce a match between modeled response and the measured response of an unknown object. When this optimization procedure is applied across a library of models for possible UXO, the correct identity of the unknown object can be ascertained because the corresponding library member will produce the closest match. Furthermore, responses from clutter objects are shown to produce very poor matches to library objects, thus providing a method to discriminate UXO from clutter. These optimization experiments are conducted on measurements of UXO in air, UXO in air but obscured by clutter fragments, buried UXO, and buried UXO obscured by clutter fragments. It is shown that the optimization procedure is successful for shallow buried objects obscured by light clutter contributing to roughly 20 dB SNR, but is limited in applicability towards very deeply buried UXO or those in dense clutter environments. The general conclusion forwarded by this work is that while increasingly accurate discrimination capabilities can be produced through accurate forward modeling and application of robust optimization and learning algorithms, the presence of noise and clutter is still of great concern. Minimization or filtering of such noise is necessary before field deployable discrimination techniques can be realized.

Subject Categories:

  • Ammunition and Explosives

Distribution Statement:

APPROVED FOR PUBLIC RELEASE